Holistic Adversarial Robustness of Deep Learning Models

AAAI 2023 pp. 15411-15420

doi:10.1609/AAAI.V37I13.26797 /aaai/2023/chen2023aaai-holistic/

Abstract

Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability. With the proliferation of deep-learning-based technology, the potential risks associated with model development and deployment can be amplified and become dreadful vulnerabilities. This paper provides a comprehensive overview of research topics and foundational principles of research methods for adversarial robustness of deep learning models, including attacks, defenses, verification, and novel applications.

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Cite

Text

Chen and Liu. "Holistic Adversarial Robustness of Deep Learning Models." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26797

Markdown

[Chen and Liu. "Holistic Adversarial Robustness of Deep Learning Models." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/chen2023aaai-holistic/) doi:10.1609/AAAI.V37I13.26797

BibTeX

@inproceedings{chen2023aaai-holistic,
  title     = {{Holistic Adversarial Robustness of Deep Learning Models}},
  author    = {Chen, Pin-Yu and Liu, Sijia},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {15411-15420},
  doi       = {10.1609/AAAI.V37I13.26797},
  url       = {https://mlanthology.org/aaai/2023/chen2023aaai-holistic/}
}